Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices.
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The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice.
To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.
Course 2 of 4 in the Digital Signal Processing Specialization.
What You Will Learn
- Digital filters, how they work
- Digital filter design
- Adaptive signal processing
Syllabus
WEEK 1
Module 2.1 Digital Filters
How digital filters work in time and in frequency.
WEEK 2
Module 2.2: Filter Design
Learning how to choose and design the right filter using the z-transform and numerical tools.
WEEK 3
Module 2.3: Stochastic and Adaptive Signal Processing
Analyzing and processing random signals and designing filters that adapt to unknown inputs.
